An observation of circular RNAs in bacterial RNA-seq data

RNA Binding Density on X-chromosome Differing from that on 22 Autosomes in Human

To test whether X-chromosome has unique genomic characteristics, X-chromosome and 22 autosomes were compared for RNA binding density. Nucleotide sequences on the chromosomes were divided into 50kb per segment that was recoded as a set of frequency values of 7-nucleotide (7nt) strings using all possible 7nt strings (47=16384). 120 genes highly expressed in tonsil germinal center B cells were selected for calculating 7nt string frequency values of all introns (RNAs). The binding density of DNA segments and RNAs was determined by the amount of complement sequences. It was shown for the first time…

Attention based convolutional neural network for predicting RNA-protein binding sites

RNA-binding proteins (RBPs) play crucial roles in many biological processes, e.g. gene regulation. Computational identification of RBP binding sites on RNAs are urgently needed. In particular, RBPs bind to RNAs by recognizing sequence motifs. Thus, fast locating those motifs on RNA sequences is crucial and time-efficient for determining whether the RNAs interact with the RBPs or not. In this study, we present an attention based convolutional neural network, iDeepA, to predict RNA-protein binding sites from raw RNA sequences. We first encode RNA sequences into one-hot encoding. Next, we design …

A Robust and Precise ConvNet for small non-coding RNA classification (RPC-snRC)

Functional or non-coding RNAs are attracting more attention as they are now potentially considered valuable resources in the development of new drugs intended to cure several human diseases. The identification of drugs targeting the regulatory circuits of functional RNAs depends on knowing its family, a task which is known as RNA sequence classification. State-of-the-art small noncoding RNA classification methodologies take secondary structural features as input. However, in such classification, feature extraction approaches only take global characteristics into account and completely oversigh…

Bayesian identification of bacterial strains from sequencing data

Rapidly assaying the diversity of a bacterial species present in a sample obtained from a hospital patient or an evironmental source has become possible after recent technological advances in DNA sequencing. For several applications it is important to accurately identify the presence and estimate relative abundances of the target organisms from short sequence reads obtained from a sample. This task is particularly challenging when the set of interest includes very closely related organisms, such as different strains of pathogenic bacteria, which can vary considerably in terms of virulence, res…

SEK: Sparsity exploiting $k$-mer-based estimation of bacterial community composition

Motivation: Estimation of bacterial community composition from a high-throughput sequenced sample is an important task in metagenomics applications. Since the sample sequence data typically harbors reads of variable lengths and different levels of biological and technical noise, accurate statistical analysis of such data is challenging. Currently popular estimation methods are typically very time consuming in a desktop computing environment. Results: Using sparsity enforcing methods from the general sparse signal processing field (such as compressed sensing), we derive a solution to the commun…

A bioinformatics pipeline for the identification of CHO cell differential gene expression from RNA-Seq data

In recent years the publication of genome sequences for the Chinese hamster and Chinese hamster ovary (CHO) cell lines have facilitated study of these biopharmaceutical cell factories with unprecedented resolution. Our understanding of the CHO cell transcriptome, in particular, has rapidly advanced through the application of next-generation sequencing (NGS) technology to characterise RNA expression (RNA-Seq). In this chapter we present a computational pipeline for the analysis of CHO cell RNA-Seq data from the Illumina platform to identify differentially expressed genes. The example data and b…

RIBOSPAN：用于多功能RNA建模的长上下文RNA基础模型

RIBOSPAN: A Long-Context RNA Foundation Model for Versatile RNA Modeling

本研究提出支持10240nt上下文的长RNA基础模型RIBOSPAN，结合多种技术实现高分辨率长RNA建模，在多任务评估中达最优性能，并开发基于其主干的mRNA生成与优化框架。

RNA亚细胞定位预测综述

A Comprehensive Review on RNA Subcellular Localization Prediction

本文综述了基于AI和机器学习预测RNA亚细胞定位的最新方法，涵盖序列、图像及混合方法，并讨论了数据稀缺等挑战及其应对机会。

Separating Putative Pathogens from Background Contamination with Principal Orthogonal Decomposition: Evidence for Leptospira in the Ugandan Neonatal Septisome

Neonatal sepsis (NS) is responsible for over a 1 million yearly deaths worldwide. In the developing world NS is often treated without an identified microbial pathogen. Amplicon sequencing of the bacterial 16S rRNA gene can be used to identify organisms that are difficult to detect by routine microbiological methods. However, contaminating bacteria are ubiquitous in both hospital settings and research reagents, and must be accounted for to make effective use of these data. In the present study, we sequenced the bacterial 16S rRNA gene obtained from blood and cerebrospinal fluid (CSF) of 80 neon…

PanDelos-plus：用于泛基因组分析中序列同源性计算的并行算法

PanDelos-plus: A parallel algorithm for computing sequence homology in pangenomic analysis

针对泛基因组同源基因家族鉴定的计算瓶颈，提出PanDelos-plus并行算法，通过数据分解和线程池并行化关键阶段，实现14倍加速和96%内存节省，使大规模分析可在普通工作站运行。

Massively Parallel RNA Chemical Mapping with a Reduced Bias MAP-seq Protocol

Chemical mapping methods probe RNA structure by revealing and leveraging correlations of a nucleotide's structural accessibility or flexibility with its reactivity to various chemical probes. Pioneering work by Lucks and colleagues has expanded this method to probe hundreds of molecules at once on an Illumina sequencing platform, obviating the use of slab gels or capillary electrophoresis on one molecule at a time. Here, we describe optimizations to this method from our lab, resulting in the MAP-seq protocol (Multiplexed Accessibility Probing read out through sequencing), version 1.0. The prot…